{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "fc67605d-db38-4c2e-a862-240e825f1e1d",
   "metadata": {},
   "source": [
    "<h1><b></v>A practitioner's guide to Triton</b></h1>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d5472f00-d80e-4099-a6e0-0acd8cbee75e",
   "metadata": {},
   "source": [
    "By UmerHA (https://x.com/UmerHAdil // https://github.com/UmerHA/), for the cuda-mode group ❤️ May our brrrr level reach over 9000."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8a15741b-fc97-40d6-b2d3-9e912b5cfa12",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "19525480-2ca8-45a6-91c4-34e97f59198e",
   "metadata": {},
   "source": [
    "# Why & when to use Triton"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a9d86769-062a-42b6-b8cf-31186b597341",
   "metadata": {},
   "source": [
    "**What is Triton**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cd02a8c5-8499-449f-bd08-6e0699a4034b",
   "metadata": {},
   "source": [
    "In short: Triton is a language to program GPUs more conventiently. You write Python-ish code, which is then compiled into ptx code (the same thing cuda code is compiled into).\n",
    "\n",
    "During the compilation, the Triton compiler tries to use clever tricks to rearrange the parts of your program (without changing the program's meaning!) to make it run faster. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0bbba9bb-e298-4ac3-b277-f72de9bd3a13",
   "metadata": {},
   "source": [
    "**Triton vs Cuda**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "60575d99-b4af-4383-b94c-d7dc2ac1189b",
   "metadata": {},
   "source": [
    "<img src='images/1_cuda_v_triton.png'>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6778f071-9dac-408b-980b-860b82b32ba2",
   "metadata": {},
   "source": [
    "source: https://zhuanlan.zhihu.com/p/672086654"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "58e290a1-7bdd-407d-a4d6-c3f34f1309a9",
   "metadata": {},
   "source": [
    "CUDA is a high-end tool with many settings for the pros.\n",
    "- full control over everything, so absolute max performance possible\n",
    "- harder to get decent performance\n",
    "- way more tedious to write and debug\n",
    "- more complicated, so harder to learn\n",
    "\n",
    "\n",
    "Triton is a  very good tool for most users\n",
    "- you can't control everything, as some things are left to automatic optimization; so you probably won't get absolute max performance\n",
    "- way easier to get good performance\n",
    "- way easier to write and debug\n",
    "- easier to learn, as it has a Python-like syntax"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "835c1a1d-ff40-4139-966f-293a87d5599b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "402ad245-5bc2-40ae-a006-a9cdfa006491",
   "metadata": {},
   "source": [
    "**Triton vs torch.compile**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aa3ca0c7-dad6-443a-a447-d867b3385e66",
   "metadata": {},
   "source": [
    "`torch.compile` makes your model faster by trying to use existing kernels more effectively and creating simple new kernels. This may make your model fast enough. If not, you can decide to invest time to write faster Triton kernels.\n",
    "\n",
    "(These simple new kernels that `torch.compile` creates are actually Triton kernels. So they are a good starting point for your custom kernels. See [Mark Saroufim](https://twitter.com/marksaroufim)'s [lecture 1 of cuda mode](https://www.youtube.com/watch?v=LuhJEEJQgUM&t=2200s) for how.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a9f100d3-061f-457a-8793-d28ac568bbe0",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "20ba90bb-c2f5-4f77-9f0e-8382f426876a",
   "metadata": {},
   "source": [
    "**When to use Triton**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e9bcf909-ad45-49e9-a9b0-8cc834d5e860",
   "metadata": {},
   "source": [
    "You start with your AI model.\n",
    "1. If it's not fast enough, `torch.compile` it.\n",
    "2. If it's not fast enough, check if you can rewrite your code to make it more suitable for `torch.compile`.\n",
    "3. If it's not fast enough, check which parts are slow and write custom Triton kernel(s) for those.\n",
    "4. If it's not fast enough, check which parts are slow and write custom CUDA kernel(s) for those.\n",
    "\n",
    "(In the unlikely case you know beforehand tyou need absolute max performance, you can decide to directly start with CUDA.) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b1fc95f7-ca8e-4baf-b8db-03f610150681",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "8608bbec-cc24-40c0-8bbf-3a2211287060",
   "metadata": {},
   "source": [
    "**A note on rough edges**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d174fa93-67e5-44fc-a5a1-aafe5241c0da",
   "metadata": {},
   "source": [
    "As Triton is a newer project, people have found it to have a few rough edges. I have noted all rough edges I encountered with the comment \"Weirdness: <description of what's weird to me>\".\n",
    "\n",
    "I expect it to get a lot more polished over time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "519c719c-212a-48f6-bcc9-6c344b5d85e1",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "10cd2baf-5e10-4143-89a9-ae62cdef79ca",
   "metadata": {},
   "source": [
    "# How to write Triton kernels"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0b39d927-a060-43b0-b40b-7c0fb1c0cf3b",
   "metadata": {},
   "source": [
    "Unlike with CUDA, we can debug Triton kernels just like any CPU program, if we set the environment variable `TRITON_INTERPRET = 1`. Then Triton runs on the CPU but simulates that it runs on the GPU.\n",
    "\n",
    "I recommend writing all programs in the simulator first, and checking for correctness. If correct, then you can make it fast.\n",
    "\n",
    "Below are some utility functions for debugging:\n",
    "- `check_tensors_gpu_ready`: (i) assert all tensors are contiguous in memory and (ii) only if not simulating, assert all tensors are on gpu\n",
    "- `breakpoint_if`: set a breakpoint, depending on conditions on pids\n",
    "- `print_if` print sth, depending on conditions on pids"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "dc39fdc4-3120-4987-93e3-0eb0ab4b956c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "from IPython.core.debugger import set_trace\n",
    "\n",
    "os.environ['TRITON_INTERPRET'] = '1' # needs to be set *before* triton is imported\n",
    "\n",
    "def check_tensors_gpu_ready(*tensors):\n",
    "    for t in tensors:\n",
    "        assert t.is_contiguous, \"A tensor is not contiguous\"\n",
    "        if not os.environ.get('TRITON_INTERPRET') == '1': assert t.is_cuda, \"A tensor is not on cuda\"\n",
    "\n",
    "def test_pid_conds(conds, pid_0=[0], pid_1=[0], pid_2=[0]):\n",
    "    '''Test if condition on pids are fulfilled\n",
    "    E.g.:\n",
    "        '=0'  checks that pid_0 == 0\n",
    "        ',>1' checks that pid_1 > 1\n",
    "        '>1,=0' checks that pid_0 > 1 and pid_1 == 0\n",
    "    '''\n",
    "    pids = pid_0[0], pid_1[0], pid_2[0]\n",
    "    conds = conds.replace(' ','').split(',')\n",
    "    for i, (cond, pid) in enumerate(zip(conds, pids)):\n",
    "        if cond=='': continue\n",
    "        op, threshold = cond[0], int(cond[1:])\n",
    "        if op not in ['<','>','>=','<=','=', '!=']: raise ValueError(f\"Rules may only use these ops: '<','>','>=','<=','=', '!='. Invalid rule: '{condition}'.\")\n",
    "        op = '==' if op == '=' else op\n",
    "        if not eval(f'{pid} {op} {threshold}'): return False\n",
    "    return True\n",
    "\n",
    "assert test_pid_conds('')\n",
    "assert test_pid_conds('>0', [1], [1])\n",
    "assert not test_pid_conds('>0', [0], [1])\n",
    "assert test_pid_conds('=0,=1', [0], [1], [0])\n",
    "\n",
    "def breakpoint_if(conds, pid_0=[0], pid_1=[0], pid_2=[0]):\n",
    "    '''Stop kernel, if any condition of pids is fulfilled'''\n",
    "    if test_pid_conds(conds, pid_0, pid_1, pid_2): set_trace()\n",
    "\n",
    "def print_if(txt, conds, pid_0=[0], pid_1=[0], pid_2=[0]):\n",
    "    '''Print txt, if any condition of pids is fulfilled'''\n",
    "    if test_pid_conds(conds, pid_0, pid_1, pid_2): print(txt)\n",
    "\n",
    "def cdiv(a,b): return (a + b - 1) // b\n",
    "assert cdiv(10,2)==5\n",
    "assert cdiv(10,3)==4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "48a65c47-a101-4cf0-93c9-8f7c741183f2",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import triton\n",
    "import triton.language as tl"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "128f01d2-e153-424e-8487-43c7b53d167b",
   "metadata": {},
   "source": [
    "# Programming model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "34892f8a-06fb-4556-9e9c-997f878c0ee2",
   "metadata": {},
   "source": [
    "With CUDA, we decompose the computation in 2 levels: First into blocks, then each block further into threads. All threads in a block run on the same SM and share the same Shared Memory. And each thread computes on **scalars**.\n",
    "\n",
    "In Triton, we decompose the computation only in 1 level: Into blocks. There is no further decomposition into threads. **Triton requires us to perform operations on vectors**. Also, we don't need to and are not able to manage the shared memory. Triton does that automatically.\n",
    "\n",
    "Example:\n",
    "\n",
    "Let's say we want to add `x` and `y`, which are vectors of size 8, and save the output into `z` (also size 8). Let's use blocks of size 4, so we have `8 / 4 = 2` blocks.\n",
    "- Cuda runs 2 blocks, each with 4 threads. Each of the 8 threads computes a single position, e.g. `z[0] = x[0] + y[0]`\n",
    "- Triton also runs 2 blocks, which each performs vectorized addition. The vector size is the block size, which is 4. E.g. `z[0:3] = x[0:3] + y[0:3]`\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9c6dc139-7d06-4558-88a3-10b19b2e5fbf",
   "metadata": {},
   "source": [
    "**All** operations in triton kernels are vectorized: Loading data, operating on data, storing data, and creating masks."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "304ed80b-700f-4f79-b44d-228e6a11fc48",
   "metadata": {},
   "source": [
    "Let's think about another simple example:\n",
    "\n",
    "Again, we want to add `x` and `y`, which are now vectors of size **6**, and save the output into `z` (also size 6). Let's use blocks of size 4, so we have `cdiv(6, 4) = 2` blocks."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "f05dde67-6374-4570-ad85-f9d8fb362ced",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(tensor([1, 2, 3, 4, 5, 6]),\n",
       " tensor([0, 1, 0, 1, 0, 1]),\n",
       " tensor([1, 3, 3, 5, 5, 7]))"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = torch.tensor([1,2,3,4,5,6])\n",
    "y = torch.tensor([0,1,0,1,0,1])\n",
    "\n",
    "x, y, x+y"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6be2aec9-6b7f-4647-b223-c7c0080c5275",
   "metadata": {},
   "source": [
    "The cuda kernel would be the C-equivalent of this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "6a4c3f94-ed61-4c3e-bf65-82fb0b971751",
   "metadata": {},
   "outputs": [],
   "source": [
    "# x,y = input tensors, z = output tensors, n = size of x, bs = block size\n",
    "def add_cuda_k(x, y, z, n, bs):\n",
    "    # locate which part of the overall computation this specific kernel is doing\n",
    "    block_id = ... # in our example: one of [0,1] \n",
    "    thread_id = ... # in our example: one of [0,1,2,3] \n",
    "\n",
    "    # identify the location of the data this specific kernel needs\n",
    "    offs = block_id * bs + thread_id\n",
    "    \n",
    "    # guard clause, to make sure we're not going out of bounds\n",
    "    if offs < n:\n",
    "\n",
    "        # read data\n",
    "        x_value = x[offs]\n",
    "        y_value = y[offs]\n",
    "        \n",
    "        # do operation\n",
    "        z_value = x_value + y_value\n",
    "        \n",
    "        # write data\n",
    "        z[offs] = z_value\n",
    "\n",
    "    # Important: offs, x_value, y_value, x_value are all scalars!\n",
    "    # The guard condition kind of is also a scalar, as it check one condition on one value."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d6ce59e2-9bac-47fd-a561-7178ceb3cba4",
   "metadata": {},
   "source": [
    "For illustration, here are the variables for every kernel:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5abcb553-e20a-463c-a9dc-24f95364129b",
   "metadata": {},
   "source": [
    "<img src='images/2_cuda_variables.png'>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f6b22698-bace-49c6-81ab-0d4258438fda",
   "metadata": {},
   "source": [
    "Let's now look at the corresponding triton kernel, which roughly looks like this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "152b589d-3e09-416f-8215-f5b3ca66d034",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Note: this is for illustration, and not quite syntactically correct. See further below for correct triton syntax\n",
    "\n",
    "def add_triton_k(x, y, z, n, bs):\n",
    "    # locate which part of the overall computation this specific kernel is doing\n",
    "    block_id = tl.program_id(0)  # in our example: one of [0,1] \n",
    "    \n",
    "    # identify the location of the data this specific kernel needs\n",
    "    offs = block_id * bs + tl.arange(0, bs) # <- this is a vector!\n",
    "    \n",
    "    # the guard clause becomes a mask, which is a vector of bools\n",
    "    mask = offs < n # <- this is a vector of bools!\n",
    "    \n",
    "    # read data\n",
    "    x_values = x[offs] # <- a vector is read!\n",
    "    y_values = y[offs] # <- a vector is read!\n",
    "    \n",
    "    # do operation\n",
    "    z_value = x_value + y_value  # <- vectors are added!\n",
    "    \n",
    "    # write data\n",
    "    z[offs] = z_value  # <- a vector is written!"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f4754391-348d-4ee2-b3f8-4f76c6e03e87",
   "metadata": {},
   "source": [
    "Again, for illustration, here are the variables for every kernel:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7823c2c2-957f-4b15-a791-bc741a433fb1",
   "metadata": {},
   "source": [
    "<img src='images/3_triton_variables.png'>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "241c1e1a-e74a-4798-88fe-ebd3648fd66e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "34101046-4732-42de-8d85-619603e98a85",
   "metadata": {},
   "source": [
    "Note on jargon: In triton lingo, each kernel (which processes a block) is called a \"program\". I.e., our example above runs 2 programs. Therefore, \"block_id\" is often called \"pid\" (short for \"program id\"), but it's the same."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "09fca448-ff95-4b9e-acbd-ce5445790013",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "5ccc846d-e3e5-4ef6-8fb7-ed7aa27b3d53",
   "metadata": {},
   "source": [
    "# Example 1: Copying a tensor"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aec9dddb-37a4-4bda-b24f-05382b1e982a",
   "metadata": {},
   "source": [
    "Let's looks at some examples. To keeps things simple, we'll use very small block sizes."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eeb5d1c3-fb01-413b-ad4d-372ec834cd9b",
   "metadata": {},
   "source": [
    "Goal: Given a tensor `x` of shape (n), copy it into another tensor `z`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "a7789ca2-1246-45bb-adec-285e0efe26e6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# # This is a normal python function, which launches the triton kernels\n",
    "def copy(x, bs, kernel_fn):\n",
    "    z = torch.zeros_like(x)\n",
    "    check_tensors_gpu_ready(x, z)\n",
    "    n = x.numel()\n",
    "    n_blocks = cdiv(n, bs)\n",
    "    grid = (n_blocks,)  # how many blocks do we have? can be 1d/2d/3d-tuple or function returning 1d/2d/3d-tuple\n",
    "\n",
    "    # launch grid!\n",
    "    # - kernel_fn is the triton kernel, which we write below\n",
    "    # - grid is the grid we constructed above\n",
    "    # - x,z,n,bs are paramters that are passed into each kernel function\n",
    "    kernel_fn[grid](x,z,n,bs)\n",
    "\n",
    "    return z    "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b2ff0265-9f65-47fc-a2be-a5c4b841f253",
   "metadata": {},
   "source": [
    "**Note:** For educational purposes, the kernel below has a logic bug (but the syntax is correct). Can you spot it?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ee6e7c2a-1af8-40bd-9824-999836c86339",
   "metadata": {},
   "outputs": [],
   "source": [
    "# # This is the triton kernel:\n",
    "\n",
    "# The triton.jit decorator takes a python function and turns it into a triton kernel, which is run on the GPU.\n",
    "# Inside this function only a subset of all python ops are allowed.\n",
    "# E.g., when NOT simulating, we can't print or use breakpoints, as these don't exist on the GPU. \n",
    "@triton.jit\n",
    "# When we pass torch tensors, they are automatically converted into a pointer to their first value\n",
    "# E.g., above we passed x, but here we receive x_ptr\n",
    "def copy_k(x_ptr, z_ptr, n, bs: tl.constexpr):\n",
    "    pid = tl.program_id(0)\n",
    "    offs = tl.arange(0, bs)  # compute the offsets from the pid \n",
    "    mask = offs < n\n",
    "    x = tl.load(x_ptr + offs, mask) # load a vector of values, think of `x_ptr + offs` as `x_ptr[offs]`\n",
    "    tl.store(z_ptr + offs, x, mask) # store a vector of values\n",
    "\n",
    "    print_if(f'pid = {pid} | offs = {offs}, mask = {mask}, x = {x}', '')\n",
    "\n",
    "    # Question: What is wrong with this kernel?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "20b92432-7c71-4dc0-be91-14ad7d6fb913",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pid = [0] | offs = [0 1], mask = [ True  True], x = [1 2]\n",
      "pid = [1] | offs = [0 1], mask = [ True  True], x = [1 2]\n",
      "pid = [2] | offs = [0 1], mask = [ True  True], x = [1 2]\n"
     ]
    }
   ],
   "source": [
    "z = copy(x, bs=2, kernel_fn=copy_k)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "0cc43e72-1691-4b2d-8299-b3a915f598d1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([1, 2, 0, 0, 0, 0])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "z"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6b6318e2-8f25-48e9-8f83-a2f08eea7c48",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "26fbbe80-0bad-4520-8287-b0aa07c4b6db",
   "metadata": {},
   "source": [
    "We were not shifting the offets correcltly. We always used offsets = [0,1], but they should change with the pid."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "59d65e42-3c41-40f9-b10b-c8dbdb060577",
   "metadata": {},
   "outputs": [],
   "source": [
    "@triton.jit\n",
    "def copy_k(x_ptr, z_ptr, n, bs: tl.constexpr):\n",
    "    pid = tl.program_id(0)\n",
    "    offs = pid * n + tl.arange(0, bs)\n",
    "    mask = offs < n\n",
    "    x = tl.load(x_ptr + offs, mask)\n",
    "    tl.store(z_ptr + offs, x, mask)\n",
    "    print_if(f'pid = {pid} | offs = {offs}, mask = {mask}, x = {x}', '')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "23fa8fe8-4f0d-4346-b2bd-ed2261ca0578",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pid = [0] | offs = [0 1], mask = [ True  True], x = [1 2]\n",
      "pid = [1] | offs = [6 7], mask = [False False], x = [1 1]\n",
      "pid = [2] | offs = [12 13], mask = [False False], x = [1 1]\n"
     ]
    }
   ],
   "source": [
    "z = copy(x, bs=2, kernel_fn=copy_k)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9ae5ad1a-14ba-44f2-85a5-eb3e3beba599",
   "metadata": {},
   "source": [
    "Not quite correct. We added `pid * n`, but want to add `pid * bs`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "13676f86-af96-4d9b-b9bf-4c2787085436",
   "metadata": {},
   "outputs": [],
   "source": [
    "@triton.jit\n",
    "def copy_k(x_ptr, z_ptr, n, bs: tl.constexpr):\n",
    "    pid = tl.program_id(0)\n",
    "    offs = pid * bs + tl.arange(0, bs)\n",
    "    mask = offs < n\n",
    "    x = tl.load(x_ptr + offs, mask)\n",
    "    tl.store(z_ptr + offs, x, mask)\n",
    "    print_if(f'pid = {pid} | offs = {offs}, mask = {mask}, x = {x}', '')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "679a5e06-1120-4d40-b6b2-a88a38ee4aa5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pid = [0] | offs = [0 1], mask = [ True  True], x = [1 2]\n",
      "pid = [1] | offs = [2 3], mask = [ True  True], x = [3 4]\n",
      "pid = [2] | offs = [4 5], mask = [ True  True], x = [5 6]\n"
     ]
    }
   ],
   "source": [
    "z = copy(x, bs=2, kernel_fn=copy_k)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d2de6ae1-b75f-4c48-b3fd-c7165ef8593a",
   "metadata": {},
   "source": [
    "Yes!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "72a218c8-d074-4dae-9b19-55fee1a1f629",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(tensor([1, 2, 3, 4, 5, 6]), tensor([1, 2, 3, 4, 5, 6]))"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x, z"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8421b1ca-fcc1-4202-8190-7870f9e12ca9",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "debf9f02-368b-4842-8ea6-8973df1aa6f3",
   "metadata": {},
   "source": [
    "As we saw, writing GPU programs involves many indices, which we can easily mess up. So I highly recommend writing and debugging the kernel in simuation mode, and testing with tiny examples first!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "82ede28f-684e-477c-938a-0895a0bbcd92",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "5adf0a27-e1ad-42a9-880b-d377c5ee22a0",
   "metadata": {},
   "source": [
    "# Example 2: Greyscaling an image"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3827f902-8435-4d70-9a1e-a8b6ef4a9c42",
   "metadata": {},
   "source": [
    "_Restart kernel here_"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "99098119-6f8b-4419-852d-d4d9321286e4",
   "metadata": {},
   "source": [
    "In this example, we'll grayscale an image of a puppy. We'll see how we can work on 2d data.\n",
    "\n",
    "This works analogously for 3D data.\n",
    "\n",
    "We've adapted Jeremy Howard's example from this [colab](https://colab.research.google.com/drive/180uk6frvMBeT4tywhhYXmz3PJaCIA_uk?usp=sharing) / [youtube](https://www.youtube.com/watch?v=4sgKnKbR-WE&feature=youtu.be). So, h/t for the example and selection of puppy image."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e69f21e8-b1a7-4d28-b912-081bd79198c1",
   "metadata": {},
   "source": [
    "_Side note: Two weird things happen in this example, if we don't restart the kernel:_\n",
    "1. _torchvision can't be imported, probably due to a circular dependency. -> I currently don't know why, need to dig deeper._\n",
    "2. _the simulated triton kernel below fails, because a float can't be mutliplied to a uint vector -> Works on GPU w/o simulation, so seems to be a `TRITON_INTERPRET` bug._"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "9c959e8b-edc3-402d-a65a-9bff148d43ad",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "from urllib.request import urlretrieve\n",
    "from pathlib import Path\n",
    "\n",
    "import torch\n",
    "from torch import tensor\n",
    "import torchvision as tv\n",
    "import torchvision.transforms.functional as tvf\n",
    "from torchvision import io\n",
    "\n",
    "import triton\n",
    "import triton.language as tl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "4d086080-bc66-4651-8788-41b1614885a9",
   "metadata": {},
   "outputs": [],
   "source": [
    "def cdiv(a,b): return (a + b - 1) // b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "dae436bd-41a8-4ddf-8b95-2ac7c0b38cf8",
   "metadata": {},
   "outputs": [],
   "source": [
    "url = 'https://upload.wikimedia.org/wikipedia/commons/thumb/4/43/Cute_dog.jpg/1600px-Cute_dog.jpg?20140729055059'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "0b395337-83ce-4919-8afc-1dafe80420b8",
   "metadata": {},
   "outputs": [],
   "source": [
    "path_img = Path('puppy.jpg')\n",
    "if not path_img.exists(): urlretrieve(url, path_img)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "245ce200-8062-4741-866e-c6e14059c4cc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([3, 1066, 1600])\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "tensor([[[117, 119, 117, 113],\n",
       "         [119, 129, 129, 113],\n",
       "         [130, 126, 122, 115]],\n",
       "\n",
       "        [[ 83,  85,  85,  80],\n",
       "         [ 85,  97,  97,  82],\n",
       "         [ 98,  93,  89,  83]]], dtype=torch.uint8)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "img = io.read_image('puppy.jpg')\n",
    "print(img.shape)\n",
    "img[:2,:3,:4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ae20a094-84b9-4fb1-9ed2-f7dd5efa012d",
   "metadata": {},
   "outputs": [],
   "source": [
    "def show_img(x, figsize=(4,3), **kwargs):\n",
    "    plt.figure(figsize=figsize)\n",
    "    plt.axis('off')\n",
    "    if len(x.shape)==3: x = x.permute(1,2,0)  # CHW -> HWC\n",
    "    plt.imshow(x.cpu(), **kwargs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "d9a79414-e719-4312-89a1-0cf7ca737ca8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3, 150, 225, 33750)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "img = tvf.resize(img, 150, antialias=True)\n",
    "ch,h,w = img.shape\n",
    "ch,h,w,h*w"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "7ddffdb6-64a1-4556-9b35-2c73b37810a3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_img(img)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4ed51086-6515-4bdf-a666-1cc003df47c6",
   "metadata": {},
   "source": [
    "To work with 2d data, we'll build 2d offsets and masks. Here's an illustration how it works, e.g. for an `4x7` matrix and block sizes of `2` for each dimensions."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0faedb4a-cfd1-4807-8e2b-7acd879d6b39",
   "metadata": {},
   "source": [
    "<img src='images/4_offset_2d.png'>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b7f817fa-099e-44b6-bbd6-6cc5503c08e5",
   "metadata": {},
   "source": [
    "And in code, it looks like this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "db0a9f18-edfd-4408-b774-ca1a744e0c99",
   "metadata": {},
   "outputs": [],
   "source": [
    "@triton.jit\n",
    "def rgb2grey_k(x_ptr, out_ptr, h, w, bs0: tl.constexpr, bs1: tl.constexpr):\n",
    "    pid_0 = tl.program_id(0)\n",
    "    pid_1 = tl.program_id(1)\n",
    "    \n",
    "    offs_0 = pid_0 * bs0 + tl.arange(0,bs0)  # 1d vector\n",
    "    offs_1 = pid_1 * bs1 + tl.arange(0,bs1)  # 1d vector\n",
    "\n",
    "    # Weirdness: None-slicing currently doesn't work when simulating on cpu. Use tl.expand_dim instead.\n",
    "    # offs = w * tl.expand_dims(offs_0, 1) + tl.expand_dims(offs_1, 0)\n",
    "    offs = w * offs_0[:,None] + offs_1[None, :]  # 2d matrix! - we multiply first offest by width, see image above\n",
    "\n",
    "    mask_0 = offs_0 < h  # 1d vector\n",
    "    mask_1 = offs_1 < w  # 1d vector\n",
    "\n",
    "    # mask = tl.expand_dims(mask_0, 1) & tl.expand_dims(mask_1, 0)\n",
    "    mask = mask_0[:,None] & mask_1[None,:]  # 2d matrix! - data musn't go out of bounds along either axis, therefore `logical and` of the individual masks\n",
    "    \n",
    "    r = tl.load(x_ptr + 0*h*w+offs, mask=mask)\n",
    "    g = tl.load(x_ptr + 1*h*w+offs, mask=mask)\n",
    "    b = tl.load(x_ptr + 2*h*w+offs, mask=mask)\n",
    "\n",
    "    # Weirdness: multiplying float with uint vectors fails when simulating on cpu\n",
    "    out = 0.2989*r + 0.5870*g + 0.1140*b  # don't worry why it's these 3 numbers we're multiplying with\n",
    "\n",
    "    tl.store(out_ptr + offs, out, mask=mask)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f76232e4-22ac-43fb-874c-fa5771f7b514",
   "metadata": {},
   "source": [
    "Let's use the kernel!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "7b042212-d697-4cc6-ab68-b6420246b801",
   "metadata": {},
   "outputs": [],
   "source": [
    "def rgb2grey(x, bs):\n",
    "    c,h,w = x.shape\n",
    "    out = torch.empty((h,w), dtype=x.dtype, device=x.device)\n",
    "\n",
    "    # grid can be a function returning a 1d/2d/3d-tuple\n",
    "    # (having a grid function is not more useful than a grid tuple in this case, but will be below when benchmarking & auto-tuning)\n",
    "    grid = lambda meta: (cdiv(h, meta['bs0']), cdiv(w,  meta['bs1']))\n",
    "    \n",
    "    rgb2grey_k[grid](x, out, h, w, bs0=bs[0], bs1=bs[1]) # all kwargs are passed into grid function\n",
    "    return out.view(h,w)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "c63352e3-e9d7-4266-9569-e9424bfe2387",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "grey_img = rgb2grey(img.to('cuda'), bs=(32, 32)).to('cpu')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "b61c5f31-35ea-420c-a17c-7357fb9c5c31",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_img(grey_img, cmap='gray')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "01154495-f3d2-46e7-b309-0a70fbca155f",
   "metadata": {},
   "source": [
    "Very cool"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cc8fc398-10f2-47d7-b339-3930ddf05432",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e58452a4-4fa5-431f-8792-856227c1c914",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "a5eff701-8bb7-46bd-b5d5-2c5b7157969c",
   "metadata": {},
   "source": [
    "# Example 3: Matmul"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bc7161a4-e71b-47cb-a12e-2d50e2e232f4",
   "metadata": {},
   "source": [
    "_For simplicity, restart kernel here_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "46abe407-01de-4173-8e13-a2f1fe5154a0",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "# os.environ['TRITON_INTERPRET'] = '1'\n",
    "\n",
    "import torch\n",
    "import triton\n",
    "import triton.language as tl\n",
    "\n",
    "# moved util functions into separate file for better readability\n",
    "from triton_util import cdiv, breakpoint_if, print_if, check_tensors_gpu_ready"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f7278a18-a60b-460b-89db-70c353ba7a61",
   "metadata": {},
   "source": [
    "Now, let's implement a naive matmul in Triton. We'll learn:\n",
    "- A method to split computation \n",
    "- Calling functions from our kernel \n",
    "- Using pre-implemented vector/matrix ops within an block\n",
    "\n",
    "This is adapted from the [OpenAI blog post announcing Triton](https://openai.com/research/triton)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "130dc03d-87cf-41bb-9ba0-e2ecd7788fe6",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "1f812df0-f5b0-4f63-b864-e539f31cf9f0",
   "metadata": {},
   "source": [
    "We want to multiply the `m x k`-matrix `A` and the `k x n`-matrix `B` into the `m x n`-matrix `C`.\n",
    "\n",
    "We split the computation along each of the three axes:\n",
    "- along the m axis - we'll use block dimension 0 to represent this\n",
    "- along the n axis - we'll use block dimension 1 to represent this\n",
    "- along the shared k axis - this will not be represented by a block. All chunks of computation will be done in same block."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9953b043-82f3-4a6a-b1ae-fbb03941adf6",
   "metadata": {},
   "source": [
    "<img src='images/5_matmul_split.png'>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "63175895-4386-4c92-aa2f-0efda65b03f1",
   "metadata": {},
   "source": [
    "Because we frequently create 1d- or 2d-offets and -masks, let's put that functionality into utility functions. As long as these functions are `triton.jit`-ed, they can be used in the kernel."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f1e8a32d-5036-4013-8c81-bf23aa91e58a",
   "metadata": {},
   "outputs": [],
   "source": [
    "@triton.jit\n",
    "def get_1d_offest(size, n_prev_chunks):\n",
    "    return n_prev_chunks * size + tl.arange(0, size)\n",
    "\n",
    "@triton.jit\n",
    "def get_2d_offset(offs_0, offs_1, stride_0, stride_1=1): \n",
    "    return tl.expand_dims(offs_0, 1)*stride_0 + tl.expand_dims(offs_1, 0)*stride_1\n",
    "\n",
    "@triton.jit\n",
    "def get_1d_mask(offs, max):\n",
    "    return offs < max\n",
    "\n",
    "@triton.jit\n",
    "def get_2d_mask(offs_0, offs_1, max_0, max_1):\n",
    "    return (tl.expand_dims(offs_0, 1) < max_0) & (tl.expand_dims(offs_1, 0) < max_1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7d14f34c-b0f0-4cb6-a8f2-d61992140520",
   "metadata": {},
   "source": [
    "Here's the naive matmul kernel:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "3626cfcd-61e9-46d5-8aca-55c2ff6b2dc7",
   "metadata": {},
   "outputs": [],
   "source": [
    "@triton.jit\n",
    "def naive_matmul_k(\n",
    "    a_ptr, b_ptr, c_ptr,\n",
    "    m, n, k,\n",
    "    stride_am, stride_ak, \n",
    "    stride_bk, stride_bn,\n",
    "    stride_cm, stride_cn,\n",
    "    bm: tl.constexpr, bn: tl.constexpr, bk: tl.constexpr\n",
    "):\n",
    "    pid_m, pid_n = tl.program_id(0), tl.program_id(1)\n",
    "    # chunks along m/n/k dimensions\n",
    "    rm = get_1d_offest(size=bm, n_prev_chunks=pid_m)\n",
    "    rn = get_1d_offest(size=bn, n_prev_chunks=pid_n)\n",
    "    rk = get_1d_offest(size=bk, n_prev_chunks=0)\n",
    "    # relevant offsets of a, b\n",
    "    offs_a = a_ptr + get_2d_offset(rm, rk, stride_am, stride_ak)\n",
    "    offs_b = b_ptr + get_2d_offset(rk, rn, stride_bk, stride_bn)\n",
    "    # initialize and iteratively update accumulator\n",
    "    acc = tl.zeros((bm, bn), dtype=tl.float32)\n",
    "    for _ in range(0, k, bk):\n",
    "        # todo umer: don't we need mask when loading a & b?\n",
    "        a = tl.load(offs_a)\n",
    "        b = tl.load(offs_b)\n",
    "        acc += tl.dot(a, b, allow_tf32=False) # matmul in block ; Weirdness: allow_tf32 must be set to False for older GPUs, otherwise won't compile\n",
    "        # increase offets, so next iteration loads next chunks\n",
    "        offs_a += bk * stride_ak\n",
    "        offs_b += bk * stride_bk\n",
    "    c = c_ptr + get_2d_offset(rm, rn, stride_cm, stride_cn)\n",
    "    mask = get_2d_mask(rm, rn, m, n)\n",
    "    tl.store(c, acc, mask=mask)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "5b325bb0-6544-4248-bdd7-28de92ef24fa",
   "metadata": {},
   "outputs": [],
   "source": [
    "from functools import partial\n",
    "\n",
    "def matmul(a, b, matmul_k_fn, bs=16, group_sz=None):\n",
    "    assert a.shape[1] == b.shape[0], \"matrix dims not compatible for matmul\"\n",
    "    check_tensors_gpu_ready(a, b)\n",
    "    (m, k), (_, n) = a.shape, b.shape\n",
    "    c = torch.empty((m, n), device=a.device, dtype=torch.float16)\n",
    "    grid = lambda meta: (triton.cdiv(m, meta['bm']),  triton.cdiv(n, meta['bn']))\n",
    "    group_sz = {} if group_sz is None else {\"group_sz\":group_sz} # not used in naive_matmul, but will be in grouped_matmul further below \n",
    "    matmul_k_fn[grid](\n",
    "        a, b, c,\n",
    "        m, n, k,\n",
    "        a.stride(0), a.stride(1),\n",
    "        b.stride(0), b.stride(1),\n",
    "        c.stride(0), c.stride(1),\n",
    "        bm=bs, bn=bs, bk=bs, # Weirdness: allow_tf32 must be set to False for older GPUs, otherwise won't compile\n",
    "        **group_sz\n",
    "    )\n",
    "    return c\n",
    "\n",
    "naive_matmul = partial(matmul, matmul_k_fn=naive_matmul_k)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "18ec2080-9f53-4ccf-a081-7dbc989863ba",
   "metadata": {},
   "outputs": [],
   "source": [
    "a = torch.ones((3, 4), dtype=torch.float32, device='cuda')\n",
    "b = torch.ones((4, 5), dtype=torch.float32, device='cuda')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "16378565-3902-45a8-8cbb-ec5bd4a05383",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[4., 4., 4., 4., 4.],\n",
       "        [4., 4., 4., 4., 4.],\n",
       "        [4., 4., 4., 4., 4.]], device='cuda:0', dtype=torch.float16)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "naive_matmul(a,b)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c3b16622-9e99-49a6-990a-7faf2b0a94fa",
   "metadata": {},
   "source": [
    "Let's unit test this against PyTorch's implementation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "8750b295-4ba0-47d6-91fd-20822587050c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "✅ Triton and Torch match\n"
     ]
    }
   ],
   "source": [
    "torch.manual_seed(0)\n",
    "a = torch.randn((512, 512), device='cuda', dtype=torch.float16)\n",
    "b = torch.randn((512, 512), device='cuda', dtype=torch.float16)\n",
    "triton_output = naive_matmul(a, b)\n",
    "torch_output = torch.matmul(a, b)\n",
    "if torch.allclose(triton_output, torch_output, atol=5e-2, rtol=0):\n",
    "    print(\"✅ Triton and Torch match\")\n",
    "else:\n",
    "    print(\"❌ Triton and Torch differ\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "de3debc9-29d2-42d0-b6e3-c542945fa95b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "300462cf-4733-4292-bf5b-303675279d1b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "b14e03db-9fb7-4031-8ef5-9da3c8fe84a2",
   "metadata": {},
   "source": [
    "# Example 4: Faster Matmul"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "869d4a2f-56b2-419a-9268-b6fc8ee3fe3b",
   "metadata": {},
   "source": [
    "_Note: Needs code from example 3, so run that before_"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "280afa03-1b13-42f3-ab19-a0233d68fee4",
   "metadata": {},
   "source": [
    "Triton handles the order of memory access **within** blocks, but not **across** blocks. So this is a knob we can use to make our kernels faster.\n",
    "\n",
    "In fact, cleverly reordering blocks can increase L2-cache hit rate, which makes our kernels faster. This example is taken from the [triton docs](https://triton-lang.org/main/getting-started/tutorials/03-matrix-multiplication.html)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae9f3487-e397-4252-a224-65bad7794a89",
   "metadata": {},
   "source": [
    "Now, to make better use of the L2 cache, we want to reuse data that's was recently loaded, and is therefore likely still in the L2 cache. How? By reducing the number of _different_ data loads that a bunch of \"consecutive\" kernels need. By \"consecutive\" we mean kernels that are executed approximately at the same time.\n",
    "\n",
    "This picture (adapter from the [triton docs](https://triton-lang.org/main/getting-started/tutorials/03-matrix-multiplication.html)) shows how we can do that. If we order naively, the first row of the output matrix is computed \"consecutively\", which needs 90 different block reads (9 from matrix A, 81 from matrix B). If we use \"group ordering\", a 3x3 square of blocks of the output matrix is computed \"consecutively\", which needs 54 different block reads (27 from matrix A, 27 from matrix B)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "be3e9c0e-52d2-4460-af1a-3b51ea12231a",
   "metadata": {},
   "source": [
    "<img src='images/6_matmul_order.png'>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6c1ac57b-9bdb-49ff-ba7b-505a00cc6b52",
   "metadata": {},
   "source": [
    "_Note: In the docs, grouping is called \"super-grouping\"_"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b6c75d7b-ba98-477f-a739-7077ee348801",
   "metadata": {},
   "source": [
    "Okay, how can we tell Triton in which order to process blocks? The answer is: We take the pids, change them, and use them as if they were the original pids.\n",
    "\n",
    "Let's do a minimal example to illustrate this principle:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "25d3ead8-a2de-4cce-a435-71aaeb99d6b1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I'm processing item 0\n",
      "I'm processing item 1\n",
      "I'm processing item 2\n",
      "I'm processing item 3\n",
      "I'm processing item 4\n"
     ]
    }
   ],
   "source": [
    "def process_item(id): print(f\"I'm processing item {id}\")\n",
    "\n",
    "for i in range(5): process_item(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "6f4ff8a5-787b-480b-9556-22919b3fba3d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I'm processing item 5\n",
      "I'm processing item 4\n",
      "I'm processing item 3\n",
      "I'm processing item 2\n",
      "I'm processing item 1\n"
     ]
    }
   ],
   "source": [
    "def change_id(old_id): return 5-old_id\n",
    "\n",
    "for i in range(5): process_item(change_id(i))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "16ddad6f-acc9-4305-ba8a-089bc11d6e53",
   "metadata": {},
   "source": [
    "Et voilà, the items were processed in a different order."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3afadaea-d07b-4645-af81-260be56f2cce",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "11fabefc-d72b-4a6f-a1b5-15fd9b39e826",
   "metadata": {},
   "source": [
    "So how should the pid-change-function for faster matmul look like? It should change the left matrix into the right matrix."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6203104d-aaa9-4a82-b4df-7a3c5db5e511",
   "metadata": {},
   "source": [
    "<img src='images/7_swizzling_exmple.png'>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3d29396e-4028-4151-84fd-30fc6b089390",
   "metadata": {},
   "source": [
    "On the left, the default ordering is shown (called \"row-major\"). Remember, we deal with blocks. We can't arrange how the individual cells are processed, only the blocks. In the picture, our output matrix C has `5x7 = 35` cells, but only `cdiv(5,1) x cdiv(7,2) = 5x4 = 20` blocks.\n",
    "\n",
    "On the right, notice how the first 9 processed blocks are the `3x3` grid we want! We process 3 blocks in a column. Then advance a column, again process 3, advance, and so on. The orange lines show where advance. This operation is called **\"swizzling\"**.\n",
    "\n",
    "By the way, you can of course change the number 3. It's called the `group_size`."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "99342eae-0aa0-4ce1-86ab-ea92dde81211",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "43c23d71-a321-487b-ac42-bc997b8dc943",
   "metadata": {},
   "source": [
    "You don't need to write swizzling yourself, as  there is a `triton.language.swizzle2d` function.\n",
    "\n",
    "To really understand `swizzle2d`, let's quickly verifiy it works as expected. We'll then continue to use it in our faster matmul kernel."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "67b54510-d04f-4436-adb2-ca73ba4b1731",
   "metadata": {},
   "source": [
    "_Side-Goal:_ Use `swizzle2d` on a `5x4` matrix with elements `0 ... 19` in row-major ordering. We should then get a matrix with elements in grouped ordering."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "027afac9-9bb5-4c1d-8671-741d8b166675",
   "metadata": {},
   "outputs": [],
   "source": [
    "@triton.jit\n",
    "def swizzle_k(x_ptr, z_ptr, group_sz: tl.constexpr):\n",
    "    pid_m, pid_n = tl.program_id(0), tl.program_id(1)\n",
    "    num_pid_m, num_pid_n = tl.num_programs(0), tl.num_programs(1)\n",
    "\n",
    "    pid_m_, pid_n_ = tl.swizzle2d(pid_m, pid_n, num_pid_m, num_pid_n, group_sz)  # Weirdness: tl.swizzle2d doesn't work when simulating on CPU\n",
    "    \n",
    "    offs_m = get_1d_offest(1, n_prev_chunks=pid_m)\n",
    "    offs_n = get_1d_offest(1, n_prev_chunks=pid_n)\n",
    "    \n",
    "    offs = get_2d_offset(offs_m, offs_n, stride_0=num_pid_n)\n",
    "    mask = get_2d_mask(offs_m, offs_n, max_0=num_pid_m, max_1=num_pid_n )\n",
    "\n",
    "    offs_sw_m = get_1d_offest(1, n_prev_chunks=pid_m_)\n",
    "    offs_sw_n = get_1d_offest(1, n_prev_chunks=pid_n_)\n",
    "    \n",
    "    offs_sw = get_2d_offset(offs_sw_m, offs_sw_n, stride_0=num_pid_n)\n",
    "    mask_sw = get_2d_mask(offs_sw_m, offs_sw_n, max_0=num_pid_m, max_1=num_pid_n)\n",
    "    \n",
    "    x = tl.load(x_ptr + offs, mask=mask)\n",
    "    tl.store(z_ptr + offs_sw, x, mask=mask_sw)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "5f9b84d1-5740-4b75-85e5-abed693a305c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[ 0,  1,  2,  3],\n",
       "        [ 4,  5,  6,  7],\n",
       "        [ 8,  9, 10, 11],\n",
       "        [12, 13, 14, 15],\n",
       "        [16, 17, 18, 19]], device='cuda:0')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "blocks_m, blocks_n = 5,4\n",
    "\n",
    "x = torch.arange(blocks_m*blocks_n, device='cuda').view(blocks_m,blocks_n)\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "8621717b-5173-4eb3-a14a-3ac823afddd4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[-1, -1, -1, -1],\n",
       "        [-1, -1, -1, -1],\n",
       "        [-1, -1, -1, -1],\n",
       "        [-1, -1, -1, -1],\n",
       "        [-1, -1, -1, -1]], device='cuda:0')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "z = -torch.ones_like(x) # empty matrix, with -1 denoting empty\n",
    "z"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "ded2fc2f-2e99-4735-9bf4-8d2e3f840578",
   "metadata": {},
   "outputs": [],
   "source": [
    "# swizzle x into z\n",
    "swizzle_k[(blocks_m,blocks_n)](x,z, group_sz=3);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "53a177f8-0ee2-4a10-b95e-9ea293f8928b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[ 0,  3,  6,  9],\n",
       "        [ 1,  4,  7, 10],\n",
       "        [ 2,  5,  8, 11],\n",
       "        [12, 14, 16, 18],\n",
       "        [13, 15, 17, 19]], device='cuda:0')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "z"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ddf9cbc5-9118-4b3f-b7ef-92be45139e72",
   "metadata": {},
   "source": [
    "Looks good!"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8c31e53a-0de9-40de-b498-7206598a2aaf",
   "metadata": {},
   "source": [
    "___"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "00b10c98-766b-45bf-b44f-839630c37f46",
   "metadata": {},
   "source": [
    "Let's now implement the grouped matmul kernel, which will be faster than the regular matmul."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "10509f97-fe4d-4e32-860a-39ee833bacbc",
   "metadata": {},
   "outputs": [],
   "source": [
    "@triton.jit\n",
    "def grouped_matmul_k(\n",
    "    a_ptr, b_ptr, c_ptr,\n",
    "    m, n, k,\n",
    "    stride_am, stride_ak, \n",
    "    stride_bk, stride_bn,\n",
    "    stride_cm, stride_cn,\n",
    "    bm: tl.constexpr, bn: tl.constexpr, bk: tl.constexpr, group_sz: tl.constexpr\n",
    "):\n",
    "    pid_m, pid_n = tl.program_id(0), tl.program_id(1)\n",
    "    num_pid_m, num_pid_n = tl.num_programs(0), tl.num_programs(1)\n",
    "    # determine location of block in grouped ordering - swizzle! \n",
    "    pid_m, pid_n = tl.swizzle2d(pid_m, pid_n, num_pid_m, num_pid_n, group_sz)  # Weirdness: tl.swizzle2d doesn't work when simulating on CPU\n",
    "    # chunks along m/n/k dimensions\n",
    "    rm = get_1d_offest(size=bm, n_prev_chunks=pid_m)\n",
    "    rn = get_1d_offest(size=bn, n_prev_chunks=pid_n)\n",
    "    rk = get_1d_offest(size=bk, n_prev_chunks=0)\n",
    "    # relevant offsets of a, b\n",
    "    offs_a = a_ptr + get_2d_offset(rm, rk, stride_am, stride_ak)\n",
    "    offs_b = b_ptr + get_2d_offset(rk, rn, stride_bk, stride_bn)\n",
    "    # initialize and iteratively update accumulator\n",
    "    acc = tl.zeros((bm, bn), dtype=tl.float32)\n",
    "    for _ in range(0, k, bk):\n",
    "        # todo umer: don't we need mask when loading a & b?\n",
    "        a = tl.load(offs_a)\n",
    "        b = tl.load(offs_b)\n",
    "        acc += tl.dot(a, b, allow_tf32=False) # block level matrix multiplication ; Weirdness: allow_tf32 must be set to False for older GPUs, otherwise won't compile\n",
    "        # increase offets, so next iteration loads next chunks\n",
    "        offs_a += bk * stride_ak\n",
    "        offs_b += bk * stride_bk\n",
    "    c = c_ptr + get_2d_offset(rm, rn, stride_cm, stride_cn)\n",
    "    mask = get_2d_mask(rm, rn, m, n)\n",
    "    tl.store(c, acc, mask=mask)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "c2338e4a-456c-40e8-be28-7a13f84101ae",
   "metadata": {},
   "outputs": [],
   "source": [
    "grouped_matmul = partial(matmul, matmul_k_fn=grouped_matmul_k)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "733268c2-29ae-4660-a9be-a36c27d5665e",
   "metadata": {},
   "outputs": [],
   "source": [
    "a = torch.ones((3, 4), dtype=torch.float32, device='cuda')\n",
    "b = torch.ones((4, 5), dtype=torch.float32, device='cuda')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "79960772-3459-4469-847f-9eb673a6ea7a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[4., 4., 4., 4., 4.],\n",
       "        [4., 4., 4., 4., 4.],\n",
       "        [4., 4., 4., 4., 4.]], device='cuda:0', dtype=torch.float16)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped_matmul(a,b, group_sz=4)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2d043afb-43b0-4757-a3b5-8f6090749352",
   "metadata": {},
   "source": [
    "Let's unit test this against PyTorch's implementation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "397e7e84-25df-4484-87b1-e4cffe658de8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "✅ Triton and Torch match\n"
     ]
    }
   ],
   "source": [
    "torch.manual_seed(0)\n",
    "a = torch.randn((512, 512), device='cuda', dtype=torch.float16)\n",
    "b = torch.randn((512, 512), device='cuda', dtype=torch.float16)\n",
    "triton_output = grouped_matmul(a, b, group_sz=32)\n",
    "torch_output = torch.matmul(a, b)\n",
    "if torch.allclose(triton_output, torch_output, atol=5e-2, rtol=0):\n",
    "    print(\"✅ Triton and Torch match\")\n",
    "else:\n",
    "    print(\"❌ Triton and Torch differ\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5b953c7b-6c5f-47aa-947d-8b03a434ddcf",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f0651202-7e2f-4af6-aab3-c3d525d334eb",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "43ec774d-1930-4173-a9fa-40e02da535f5",
   "metadata": {},
   "source": [
    "# Benchmarking"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d72bfe38-017e-42a8-9fc1-b5d42d45396a",
   "metadata": {},
   "source": [
    "Triton brings built-in benchmarking tools with it. Here's an example how to use it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "bb30aeb9-c025-492c-aa1f-933f92b6c724",
   "metadata": {},
   "outputs": [],
   "source": [
    "# adapted from https://triton-lang.org/main/getting-started/tutorials/01-vector-add.html\n",
    "@triton.testing.perf_report(\n",
    "    triton.testing.Benchmark(\n",
    "        x_names=['square_matrix_size'],  # Argument names to use as an x-axis for the plot.\n",
    "        x_vals=[2**i for i in range(5, 12, 1)],  # Different possible values for `x_name`.\n",
    "        x_log=True,  # x axis is logarithmic.\n",
    "        line_arg='provider',  # Argument name whose value corresponds to a different line in the plot.\n",
    "        line_vals=['naive', 'grouped', 'torch'],  # Possible values for `line_arg`.\n",
    "        line_names=['Naive', 'Grouped', 'Torch'],  # Label name for the lines.\n",
    "        styles=[('blue', '-'), ('green', '-'), ('orange','-')],  # Line styles.\n",
    "        ylabel='GB/s',  # Label name for the y-axis.\n",
    "        plot_name='matmul-performance',  # Name for the plot. Used also as a file name for saving the plot.\n",
    "        args={},  # Values for function arguments not in `x_names` and `y_name`.\n",
    "    ))\n",
    "def benchmark(square_matrix_size, provider):\n",
    "    sz = square_matrix_size\n",
    "    a = torch.rand((sz, sz), device='cuda', dtype=torch.float32)\n",
    "    b = torch.rand((sz, sz), device='cuda', dtype=torch.float32)\n",
    "    quantiles = [0.5, 0.2, 0.8]\n",
    "    if provider == 'naive':   ms, min_ms, max_ms = triton.testing.do_bench(lambda: naive_matmul(a, b), quantiles=quantiles)\n",
    "    if provider == 'grouped': ms, min_ms, max_ms = triton.testing.do_bench(lambda: grouped_matmul(a, b, group_sz=8), quantiles=quantiles)\n",
    "    if provider == 'torch':   ms, min_ms, max_ms = triton.testing.do_bench(lambda: torch.matmul(a,b), quantiles=quantiles)\n",
    "    gbps = lambda ms: 12 * sz / ms * 1e-6\n",
    "    return gbps(ms), gbps(max_ms), gbps(min_ms)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2085cdba-731c-476a-9325-a7d0aa8f0c0a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "acec5487-c05d-4da4-a7f3-f808fae779fa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "matmul-performance:\n",
      "   square_matrix_size     Naive   Grouped     Torch\n",
      "0                32.0  0.085106  0.085106  0.053691\n",
      "1                64.0  0.129730  0.125000  0.107143\n",
      "2               128.0  0.159468  0.154341  0.170515\n",
      "3               256.0  0.097909  0.099071  0.125654\n",
      "4               512.0  0.030346  0.030361  0.111079\n",
      "5              1024.0  0.006971  0.007279  0.034461\n",
      "6              2048.0  0.001405  0.001749  0.006355\n"
     ]
    }
   ],
   "source": [
    "benchmark.run(print_data=True, show_plots=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "92a65c89-febf-4672-8f8a-b907108e46a5",
   "metadata": {},
   "source": [
    "_Note Umer: I would've expected the GB/s to increase as the matrix sizes get larger. Why don't they? Maybe because share memory is full, so kernel spends more and more time reloading stuff_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "41980f55-159e-47ba-95f9-21fd7a07e625",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "a5d2da3e-6382-4275-908d-d8d195d12dff",
   "metadata": {},
   "source": [
    "Let's try different block sizes:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "2af01ab8-fb78-4420-9e32-90cf595af400",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "matmul-performance:\n",
      "   batch_size     Naive   Grouped     Torch\n",
      "0        16.0  0.030404  0.030433  0.111111\n",
      "1        32.0  0.060683  0.061127  0.111111\n",
      "2        64.0  0.083660  0.084026  0.111111\n"
     ]
    }
   ],
   "source": [
    "@triton.testing.perf_report(\n",
    "    triton.testing.Benchmark(\n",
    "        x_names=['batch_size'], x_vals=[2**i for i in range(4, 7, 1)], x_log=True,\n",
    "        line_arg='provider', line_vals=['naive', 'grouped', 'torch'], line_names=['Naive', 'Grouped', 'Torch'],\n",
    "        styles=[('blue', '-'), ('green', '-'), ('orange','-')],\n",
    "        ylabel='GB/s', plot_name='matmul-performance', args={}\n",
    "    ))\n",
    "def benchmark(batch_size, provider):\n",
    "    sz = 512\n",
    "    a = torch.rand((sz, sz), device='cuda', dtype=torch.float32)\n",
    "    b = torch.rand((sz, sz), device='cuda', dtype=torch.float32)\n",
    "    quantiles = [0.5, 0.2, 0.8]\n",
    "    if provider == 'naive':   ms, min_ms, max_ms = triton.testing.do_bench(lambda: naive_matmul(a, b, bs=batch_size), quantiles=quantiles)\n",
    "    if provider == 'grouped': ms, min_ms, max_ms = triton.testing.do_bench(lambda: grouped_matmul(a, b, bs=batch_size, group_sz=8), quantiles=quantiles)\n",
    "    if provider == 'torch':   ms, min_ms, max_ms = triton.testing.do_bench(lambda: torch.matmul(a,b), quantiles=quantiles)\n",
    "    gbps = lambda ms: 12 * sz / ms * 1e-6\n",
    "    return gbps(ms), gbps(max_ms), gbps(min_ms)\n",
    "\n",
    "benchmark.run(print_data=True, show_plots=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4718c500-56e2-418b-ba05-4c6e10bb9018",
   "metadata": {},
   "source": [
    "Larger block sizes seem to be better. Let's compare with pytorch again, using larger block sizes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "0e2eb214-3ca7-4dd8-868a-08f9bc2867b8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "matmul-performance:\n",
      "   square_matrix_size     Naive   Grouped     Torch\n",
      "0                32.0  0.039867  0.038710  0.053215\n",
      "1                64.0  0.077922  0.071006  0.106667\n",
      "2               128.0  0.109091  0.107143  0.169912\n",
      "3               256.0  0.137733  0.136364  0.126150\n",
      "4               512.0  0.084731  0.083916  0.111047\n",
      "5              1024.0  0.021879  0.025362  0.034691\n",
      "6              2048.0  0.005257  0.005919  0.007440\n"
     ]
    }
   ],
   "source": [
    "@triton.testing.perf_report(\n",
    "    triton.testing.Benchmark(\n",
    "        x_names=['square_matrix_size'], x_vals=[2**i for i in range(5, 12, 1)], x_log=True,\n",
    "        line_arg='provider', line_vals=['naive', 'grouped', 'torch'], line_names=['Naive', 'Grouped', 'Torch'],\n",
    "        styles=[('blue', '-'), ('green', '-'), ('orange','-')],\n",
    "        ylabel='GB/s', plot_name='matmul-performance', args={}\n",
    "    ))\n",
    "def benchmark(square_matrix_size, provider):\n",
    "    sz = square_matrix_size\n",
    "    a = torch.rand((sz, sz), device='cuda', dtype=torch.float32)\n",
    "    b = torch.rand((sz, sz), device='cuda', dtype=torch.float32)\n",
    "    quantiles = [0.5, 0.2, 0.8]\n",
    "    if provider == 'naive':   ms, min_ms, max_ms = triton.testing.do_bench(lambda: naive_matmul(a, b, bs=64), quantiles=quantiles)\n",
    "    if provider == 'grouped': ms, min_ms, max_ms = triton.testing.do_bench(lambda: grouped_matmul(a, b, group_sz=8, bs=64), quantiles=quantiles)\n",
    "    if provider == 'torch':   ms, min_ms, max_ms = triton.testing.do_bench(lambda: torch.matmul(a,b), quantiles=quantiles)\n",
    "    gbps = lambda ms: 12 * sz / ms * 1e-6\n",
    "    return gbps(ms), gbps(max_ms), gbps(min_ms)\n",
    "\n",
    "benchmark.run(print_data=True, show_plots=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "be721a2b-69b0-482a-9080-abefc48819c7",
   "metadata": {},
   "source": [
    "This reduces the performance difference to pytorch for larger matrix sizes, but pytorch is still better."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "12d8ea2f-e531-430f-ad76-56f3a157314e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "3dfda9b2-b203-45a7-85b1-91f08d4bfd7d",
   "metadata": {},
   "source": [
    "Tip: For profiling, we can use Nsight Compute to profile our kernels:\n",
    "`ncu --target-processes all your_python_file.py`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "61e1e4a5-b109-4fe3-bce2-e0e22e194ac8",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "6c866787-e12d-4bde-a915-113289d845b0",
   "metadata": {},
   "source": [
    "# Auto-Tuning"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fbd773e6-71f5-43c1-bb8b-bb41d1cb4b20",
   "metadata": {},
   "source": [
    "Adapted from https://triton-lang.org/main/getting-started/tutorials/03-matrix-multiplication.html"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f603e665-cb2c-4a6a-9eea-81fcfcb9952b",
   "metadata": {},
   "source": [
    "The choice of meta-parameters (e.g. block sizes) and compilation options (e.g. `num_warps`) impacts the kernel speed. Triton allows you to pass a list of possible choices, runs them all, and then compiles the kernel for the fastest choice. This is called `Auto-Tuning`.\n",
    "\n",
    "If the size of your problem changes (e.g. when matrix changes size), a new auto-tune will be done for the new problem size."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "c25f75d3-05c1-4b3a-970a-399574286a4e",
   "metadata": {},
   "outputs": [],
   "source": [
    "@triton.autotune(\n",
    "    # Choices of configs to auto-tune over\n",
    "    configs=[\n",
    "        triton.Config({'bm': 128, 'bn': 256, 'bk': 64, 'group_sz': 8}, num_stages=3, num_warps=8),\n",
    "        triton.Config({'bm': 64, 'bn': 256, 'bk': 32, 'group_sz': 8}, num_stages=4, num_warps=4),\n",
    "        triton.Config({'bm': 128, 'bn': 128, 'bk': 32, 'group_sz': 8}, num_stages=4, num_warps=4),\n",
    "        triton.Config({'bm': 128, 'bn': 64, 'bk': 32, 'group_sz': 8}, num_stages=4, num_warps=4),\n",
    "        triton.Config({'bm': 64, 'bn': 128, 'bk': 32, 'group_sz': 8}, num_stages=4, num_warps=4),\n",
    "        triton.Config({'bm': 128, 'bn': 32, 'bk': 32, 'group_sz': 8}, num_stages=4, num_warps=4),\n",
    "        triton.Config({'bm': 64, 'bn': 32, 'bk': 32, 'group_sz': 8}, num_stages=5, num_warps=2),\n",
    "        triton.Config({'bm': 32, 'bn': 64, 'bk': 32, 'group_sz': 8}, num_stages=5, num_warps=2),\n",
    "    ],\n",
    "    # Definition of problem size. If it changes, a new auto-tune is run for the new problem size.\n",
    "    key=['m', 'n', 'k'],\n",
    ")\n",
    "@triton.jit\n",
    "def grouped_autotuned_matmul_k(\n",
    "    a_ptr, b_ptr, c_ptr,\n",
    "    m, n, k,\n",
    "    stride_am, stride_ak, \n",
    "    stride_bk, stride_bn,\n",
    "    stride_cm, stride_cn,\n",
    "    bm: tl.constexpr, bn: tl.constexpr, bk: tl.constexpr, group_sz: tl.constexpr\n",
    "):\n",
    "    pid_m = tl.program_id(0)\n",
    "    pid_n = tl.program_id(1)\n",
    "    num_pid_m = tl.num_programs(0)\n",
    "    num_pid_n = tl.num_programs(1)\n",
    "    # determine location of block in grouped ordering\n",
    "    pid_m, pid_n = tl.swizzle2d(pid_m, pid_n, num_pid_m, num_pid_n, group_sz)  # Weirdness: tl.swizzle2d doesn't work when simulating on CPU\n",
    "    # chunks along m/n/k dimensions\n",
    "    rm = get_1d_offest(size=bm, n_prev_chunks=pid_m)\n",
    "    rn = get_1d_offest(size=bn, n_prev_chunks=pid_n)\n",
    "    rk = get_1d_offest(size=bk, n_prev_chunks=0)\n",
    "    # relevant offsets of a, b\n",
    "    offs_a = a_ptr + get_2d_offset(rm, rk, stride_am, stride_ak)\n",
    "    offs_b = b_ptr + get_2d_offset(rk, rn, stride_bk, stride_bn)\n",
    "    # initialize and iteratively update accumulator\n",
    "    acc = tl.zeros((bm, bn), dtype=tl.float32)\n",
    "    for _ in range(0, k, bk):\n",
    "        # todo umer: don't we need mask when loading a & b?\n",
    "        a = tl.load(offs_a)\n",
    "        b = tl.load(offs_b)\n",
    "        acc += tl.dot(a, b, allow_tf32=False) # block level matrix multiplication ; Weirdness: allow_tf32 must be set to False for older GPUs, otherwise won't compile\n",
    "        # increase offets, so next iteration loads next chunks\n",
    "        offs_a += bk * stride_ak\n",
    "        offs_b += bk * stride_bk\n",
    "    c = c_ptr + get_2d_offset(rm, rn, stride_cm, stride_cn)\n",
    "    mask = get_2d_mask(rm, rn, m, n)\n",
    "    tl.store(c, acc, mask=mask)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "f18195a7-2258-4b5a-9d1d-0361aa95953d",
   "metadata": {},
   "outputs": [],
   "source": [
    "def grouped_autotuned_matmul(a, b):\n",
    "    matmul_k_fn = grouped_autotuned_matmul_k\n",
    "    \n",
    "    assert a.shape[1] == b.shape[0], \"matrix dims not compatible for matmul\"\n",
    "    check_tensors_gpu_ready(a, b)\n",
    "    (m, k), (_, n) = a.shape, b.shape\n",
    "    c = torch.empty((m, n), device=a.device, dtype=torch.float16)\n",
    "    grid = lambda meta: (triton.cdiv(m, meta['bm']),  triton.cdiv(n, meta['bn']))\n",
    "    matmul_k_fn[grid](\n",
    "        a, b, c,\n",
    "        m, n, k,\n",
    "        a.stride(0), a.stride(1),\n",
    "        b.stride(0), b.stride(1),\n",
    "        c.stride(0), c.stride(1),\n",
    "        # bm=bs, bn=bs, bk=bs, <- will be autotuned\n",
    "        # **group_sz <- will be autotuned\n",
    "    )\n",
    "    return c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a0c5d286-b66c-46ea-a0ec-07f98cd88108",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "70efd987-4246-4aeb-83dc-6fbc367f7f6b",
   "metadata": {},
   "outputs": [],
   "source": [
    "a,b = torch.ones(3,4, device='cuda'), torch.ones(4,5, device='cuda')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "b1a43e71-56d7-4ebd-95e2-eb4d13b79969",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[4., 4., 4., 4., 4.],\n",
       "        [4., 4., 4., 4., 4.],\n",
       "        [4., 4., 4., 4., 4.]], device='cuda:0')"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a@b"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "144df98b-592d-409a-8da6-58515e16868e",
   "metadata": {},
   "source": [
    "_Note: sometimes the following line returns wrong results, and I can't reliably reproduce it. If you can, please tell me via Twitter (@UmerHAdil) ! 🙏🏽_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "c0c493bb-d747-4b61-ba2a-e2772c66c24c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[4., 4., 4., 4., 4.],\n",
       "        [4., 4., 4., 4., 4.],\n",
       "        [4., 4., 4., 4., 4.]], device='cuda:0', dtype=torch.float16)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped_autotuned_matmul(a,b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "024b8603-3791-4e01-890d-883b39813a97",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "460c4fe9-003e-4d4f-95ee-5b9af56d2ccf",
   "metadata": {},
   "source": [
    "For tips, tricks and heuristics which configs to try for auto-tuning, see [Mark Saroufim's talk \"CUDA Performance Checklist\"](https://www.youtube.com/watch?v=SGhfUhlowB4). Much of it should apply to Triton as well."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fde7978d-8409-4942-8551-f5d31e518d51",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "3d662cab-1dfc-4282-a1bd-8fe3e286fd6d",
   "metadata": {},
   "source": [
    "Let's run the benchmark once again. This will take a lot of time, as we auto-tune for each benchmarking paramater choice (i.e., 12-5=7 times for us)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "b2048baf-2516-43e1-b65e-622dd77e53f5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "matmul-performance:\n",
      "   square_matrix_size     Naive   Grouped  Grouped & Auto-Tuned     Torch\n",
      "0                32.0  0.040067  0.037500              0.062176  0.054795\n",
      "1                64.0  0.077170  0.074303              0.091954  0.104803\n",
      "2               128.0  0.110218  0.107143              0.117936  0.169912\n",
      "3               256.0  0.139738  0.136364              0.137339  0.126482\n",
      "4               512.0  0.083953  0.082937              0.066864  0.110983\n",
      "5              1024.0  0.023112  0.025932              0.020007  0.033520\n",
      "6              2048.0  0.005235  0.005912              0.004629  0.007076\n"
     ]
    }
   ],
   "source": [
    "@triton.testing.perf_report(\n",
    "    triton.testing.Benchmark(\n",
    "        x_names=['square_matrix_size'], x_vals=[2**i for i in range(5, 12, 1)], x_log=True,\n",
    "        line_arg='provider', line_vals=['naive', 'grouped', 'grouped-autotuned', 'torch'], line_names=['Naive', 'Grouped', 'Grouped & Auto-Tuned','Torch'],\n",
    "        styles=[('blue', '-'), ('green', '-'), ('green', '--'), ('orange','-')],\n",
    "        ylabel='GB/s', plot_name='matmul-performance', args={}\n",
    "    ))\n",
    "def benchmark(square_matrix_size, provider):\n",
    "    sz = square_matrix_size\n",
    "    a = torch.rand((sz, sz), device='cuda', dtype=torch.float32)\n",
    "    b = torch.rand((sz, sz), device='cuda', dtype=torch.float32)\n",
    "    quantiles = [0.5, 0.2, 0.8]\n",
    "    if provider == 'naive':   ms, min_ms, max_ms = triton.testing.do_bench(lambda: naive_matmul(a, b, bs=64), quantiles=quantiles)\n",
    "    if provider == 'grouped': ms, min_ms, max_ms = triton.testing.do_bench(lambda: grouped_matmul(a, b, group_sz=8, bs=64), quantiles=quantiles)\n",
    "    if provider == 'grouped-autotuned': ms, min_ms, max_ms = triton.testing.do_bench(lambda: grouped_autotuned_matmul(a, b), quantiles=quantiles)\n",
    "    if provider == 'torch':   ms, min_ms, max_ms = triton.testing.do_bench(lambda: torch.matmul(a,b), quantiles=quantiles)\n",
    "    gbps = lambda ms: 12 * sz / ms * 1e-6\n",
    "    return gbps(ms), gbps(max_ms), gbps(min_ms)\n",
    "\n",
    "benchmark.run(print_data=True, show_plots=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4954f5d2-7145-4109-aa9c-10208b3914e7",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "152c469b-fd36-48bf-94a1-828e2b361736",
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   "source": [
    "___"
   ]
  },
  {
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   "id": "e70eda94-213f-484d-b1a3-21a566b95a27",
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   "source": [
    "<h1>That's it! Congrats on making it through the tutorial - good work! 🥳</h1>\n",
    "\n",
    "I highly encourage you to write a few triton kernels yourself. You can e.g. try these triton puzzles: https://github.com/srush/Triton-Puzzles by [Sasha Rush](https://twitter.com/srush_nlp), Tejas Ramesh and [Keren Zhou](https://twitter.com/ZhouKeren).\n",
    "\n",
    "Here is other intermediate and advanced material:\n",
    "- The official documentation: https://triton-lang.org/\n",
    "- The LightLLM repo has a ton of real-world triton kernels: https://github.com/ModelTC/lightllm/tree/main/lightllm/common/basemodel/triton_kernel\n",
    "- So does the Unsloth repo: https://github.com/unslothai/unsloth/tree/main/unsloth/kernels"
   ]
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  {
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   "id": "07323512-dab4-461e-b765-78ba2f50ca40",
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   "source": [
    "If you're generally interested in GPU programming and performance, the [cuda mode Discord](https://discord.gg/cudamode) may be interesting to you. This tutorial was written as part of their amazing [lecture series](https://www.youtube.com/@CUDAMODE)."
   ]
  },
  {
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   "execution_count": null,
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  },
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   "source": [
    "___"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f8b7f1a8-1525-4ad5-80be-0523d7cdde47",
   "metadata": {},
   "source": [
    "**About the author:**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b4fb0eb5-7276-4ff9-adec-1e7b9f80357e",
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   "source": [
    "Hey 👋🏽 I'm Umer from Germany. Thanks for reading this tutorial. I hope you got learned a lot from it. If you have any questions, feel free to shoot me a message on Twitter ([@UmerHAdil](https://x.com/UmerHAdil)).\n",
    "\n",
    "As I currently do Open-Source AI work as an independent ML engineer, I have set up a ko-fi page for tips & donations: https://ko-fi.com/umerha.\n",
    "Apart from this guide, I've contributed to HuggingFace diffusers (e.g. [shoutouts by HF](https://x.com/RisingSayak/status/1773739194474463629)), LangChain [shoutouts by the team](https://twitter.com/search?lang=de&q=(from%3ALangChainAI)%20(%40UmerHAdil)%20lang%3Aen&src=typed_query)), and gpt-engineer (e.g. [this](https://x.com/UmerHAdil/status/1715447656527339668))."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9fad15fe-c8d5-4cbe-ac7b-022af9ebdbb0",
   "metadata": {},
   "source": [
    "If you're a company in need of Triton and/or CUDA consulting, also shoot me a message on Twitter ([@UmerHAdil](https://x.com/UmerHAdil))."
   ]
  },
  {
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   "execution_count": null,
   "id": "27847869-dd06-4a74-9b0e-7670c32d5f33",
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